Nomograms for Differentiated Thyroid Carcinoma Patients Based on the Eighth AJCC Staging and Competing Risks Model

Application and subgroup analysis of competing risks model based on different lymph node staging systems in differentiated thyroid cancer

Differentiated thyroid cancer (DTC) is the most common endocrine malignancy, with a rising incidence worldwide. Accurate prognostic models are essential for effective patient management. This study evaluates the prognostic value of various lymph node staging systems in DTC using a competing risks model. We used SEER database records (1998-2016) of 16,527 DTC patients, analyzing N stage, positive lymph node numbers (PLNNs), metastatic lymph node ratio (MLNR), log odds of positive lymph nodes (LODDS), and log odds of the negative lymph node (NLN)/T stage ratio (LONT). Univariate and multivariate analyses in a competing risks model were performed, along with subgroup analyses based on demographic and clinical characteristics. In this study of 16,527 patients with DTC, different lymph node staging systems showed different prognostic correlations in univariate and multivariate analyses. In particular, PLNNs showed significant prognostic correlations in several subgroups. Additionally, PLNNs were more suitable as a lymph node staging system for DTC than LODDS and MLNR in N1 stage subgroups, with an optimal cut-off of 13. Receiver operating characteristic curves, calibration curves and nomograms improved the clinical utility of the prognostic model based on PLNNs. Using competing risks model and subgroup analyses, we found that PLNNs had the best prognostic discriminatory efficacy for patients with DTC, especially those with N1 stage disease, and had an optimal cut-off value of 13.

Scoring system and a simple nomogram for predicting radioiodine refractory differentiated thyroid cancer: a retrospective study

Background

Differentiated thyroid carcinoma (DTC) originates from abnormal follicular cells and accounts for approximately 90–95% of thyroid malignancies. The diagnosis of radioiodine refractory DTC (RR-DTC) is based on clinical evolution and iodine uptake characteristics rather than pathological characteristics. Thus, it takes a long time to become apparent, and the definition of RR-DTC covers multiple aspects. We aimed to analyze the clinical and molecular imaging characteristics of patients with RR-DTC and identify independent predictors to develop an RR-DTC scoring system and a simple nomogram for predicting the probability of RR-DTC. We reviewed the data of 404 patients with metastatic DTC who underwent both post-RAI WB therapy scintigraphy and ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography. Data on the clinical features and molecular characteristics of RR-DTC and non-RR-DTC cases were obtained from medical records. We screened for predictors using univariate analyses, obtained independent predictors through multivariate analyses, and then established a scoring system and a simple nomogram for predicting RR-DTC according to the corresponding odds ratio (OR) values.

Results

Diagnosis at age ≥ 48 years (OR, 1.037; 95% confidence interval [CI], 1.007–1.069), recurrence between the operation and iodine-131 treatment (OR, 7.362; 95% CI 2.388–22.698), uptake of ¹⁸F-FDG (OR, 39.534; 95% CI 18.590–84.076), and the metastasis site (OR, 4.365; 95% CI 1.593–11.965) were highly independently associated with RR-DTC. We established a scoring system for predicting RR-DTC, showing that the area under the receiver operating characteristic curve (AUC) with a cutoff value of 10 points (AUC = 0.898) had a higher discernibility than any other single independent predictor. The risk factors of RR-DTC in nomogram modeling include diagnosis at age ≥ 48 years, recurrence between the operation and iodine-131 treatment, uptake of ¹⁸F-FDG, and the site of metastasis. The concordance index (c-Index) of the nomogram was 0.9.

Conclusions

We demonstrated that a predictive model based on four factors has a good ability to predict RR-DTC. An index score ≥ 10 points was found to be the optimal index point for predicting RR-DTC. Moreover, this nomogram model has good predictive ability and stability. This model may help establish an active surveillance or appropriate treatment strategy for RR-DTC cases.